Input correction in rainfall runoff models using the Ensemble Kalman filter

Köster, T.; Serafy, G.Y.H. El; Boogaard, Henk van den; Heemink, A.W.; Mynett, Arthur E.

doi:10.1201/b16814-324

Cited by 3 publications

(6 citation statements)

References 2 publications

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“…communication, 2005). Therefore we assume that the input error term with a standard deviation similar to that of Koster et al [2004],…”

Section: Specifying Errors In Forcing and Discharge Datamentioning

confidence: 99%

“…This has led to the development of the well-known ensemble Kalman Filter (EnKF) [Burgers et al, 1998;Evensen, 1994]. For flood forecasting, its potential was proven successfully by comparing it with EKF on a calibrated Rhine application ; and input uncertainty estimation was recently analyzed through the use of EnKF for the Hymod model [Koster et al, 2004]. Dual-parameter and state estimation was recently investigated using EnKF techniques [Moradkhani et al, 2005b;Vrugt, 2004].…”

Section: Introductionmentioning

confidence: 99%

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Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models

Weerts

Serafy

2006

Water Resources Research

334

367

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[1] Sequential importance resampling (SIR) filter, residual resampling filter (RR), and an ensemble Kalman (EnKF) filter that can handle dynamic nonlinear/non-Gaussian models are compared to correct erroneous model inputs and to obtain a rainfall-runoff update with a conceptual rainfall-runoff model HBV-96 for flood forecasting purposes. EnKF performs best with a low number of ensemble members. The RR filter performs best at intermediate and high number of particles, although differences are small. With all filters the rainfall error could be estimated during a synthetic experiment when the soil is not too dry and the measurement error on the discharge is not dominant. The temperature error could only be estimated when the temperature is close to 0°C. When applying these methods to a real case, good results are obtained. For low flows, EnKF outperforms both particle filters, because it is less sensitive to misspecification of the model and uncertainties. These methods are feasible and easy to implement in real flood forecasting systems. Further research on the assumptions on model uncertainties and measurement uncertainties is recommended.

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“…communication, 2005). Therefore we assume that the input error term with a standard deviation similar to that of Koster et al [2004],…”

Section: Specifying Errors In Forcing and Discharge Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models

Weerts

Serafy

2006

Water Resources Research

334

367

View full text Add to dashboard Cite

show abstract

“…An AR(1) process that will be used in order to model the errors on precipitation. This has been thoroughly discussed by Koster et al (2004). This results in the following state vector,…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The standard deviation of the perturbations was chosen as a combination of a fixed value and a percentage of the actual input value for precipitation or evapotranspiration, respectively as in Koster et al (2004). Figure 2 shows the effect of perturbations in the precipitation and evapotranspiration separately on the discharge.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…Shu et al (2005) use the ensemble Kalman filter rainfall runoff hydrological models to improve the estimate of the soil moisture. Several applications on the flood forecasting field have previously been studied, Weerts and El Serafy (2006) compare the ensemble Kalman filter and the particle filter, and Koster et al (2004) study the effect of input correction on the conceptual rainfall runoff model used in this paper (i.e. Hymod model).…”

Section: Introductionmentioning

confidence: 99%

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The ensemble particle filter (EnPF) in rainfall-runoff models

Delft

Serafy

Heemink

2009

Stoch Environ Res Risk Assess

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Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the particle filter (PF) both have their own strengths. Research was carried out to a possible combination between both types of filters that will lead to a new type of filters that joins the strengths of both. The so called ensemble particle filter (EnPF) new combination is tested on flood forecasting problems in both the hindcast mode as well as the forecast mode. Several proposed combinations showed considerable improvement when a hindcast comparison on synthetic data was considered. Within the forecast comparison with field data, the suggested EnPF showed remarkable improvements compared to the PF and slight improvements compared to the EnKF.

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Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets

Mo,

Su,

Lei

et al. 2024

Water

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Reliable runoff modeling is essential for water resource allocation and management. However, a key uncertainty source is that the true precipitation field is difficult to measure, making reliable runoff modeling still challenging. To account for this uncertainty, this study developed a two-step approach combining ensemble average and cumulative distribution correction (i.e., EC) to incorporate information from the GR4J (modèle du Génie Rural à 4 paramètres Journalier) hydrological model and multiple remotely sensed precipitation datasets. In the EC approach, firstly, the ensemble average is applied to construct transitional fluxes using the reproduced runoff information, which is yielded by applying various remotely sensed precipitation datasets to drive the GR4J model. Subsequently, the cumulative distribution correction is applied to enhance the transitional fluxes to model runoff. In our experiments, the effectiveness of the EC approach was investigated by runoff modeling to incorporate information from the GR4J model and six precipitation datasets in the Pingtang Watershed (PW; Southwest China), and the single precipitation dataset-based approaches and the ensemble average were used as benchmarks. The results show that the EC method performed better than the benchmarks and had a satisfactory performance with Nash–Sutcliffe values of 0.68 during calibration and validation. Meanwhile, the EC method exhibited a more stable performance than the ensemble averaging method under different incorporation scenarios. However, the single precipitation dataset-based approaches tended to underestimate runoff (regression coefficients < 1), and there were similar errors between the calibration and validation stages. To further illustrate the effectiveness of the EC model, five watersheds (including the PW) of different hydrometeorological features were used to test the EC model and its benchmarks. The results show that both the EC model and the ensemble averaging had good transferability, but the EC model had better performance across all the test watersheds. Conversely, the single precipitation dataset-based approaches exhibited significant regional variations and, therefore, had low transferability. The current study concludes that the EC approach can be a robust alternative to model runoff and highlights the value of the incorporation of multiple precipitation datasets in runoff modeling.

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Input correction in rainfall runoff models using the Ensemble Kalman filter

Cited by 3 publications

References 2 publications

Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models

Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models

The ensemble particle filter (EnPF) in rainfall-runoff models

Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets

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